Towards #1798 . (#2170)

* Towards #1798 .

This PR addresses the estimators inside HalLearners:

Two public extension methods, one for simple arguments and the other for advanced options
Delete unecessary constructors
Pass Options objects as arguments instead of Action delegate
Rename Arguments to Options
Rename Options objects as options (instead of args or advancedSettings used so far)

Author: sfilipi

src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs

@@ -57,7 +57,7 @@ public static FastTreeRegressionTrainer FastTree(this RegressionContext.Regressi
         /// Predict a target using a decision tree binary classification model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>.
         /// </summary>
         /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
+        /// <param name="labelColumn">The label column.</param>
         /// <param name="featureColumn">The featureColumn column.</param>
         /// <param name="weights">The optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
@@ -97,7 +97,7 @@ public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassifica
         /// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>.
         /// </summary>
         /// <param name="ctx">The <see cref="RankingContext"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
+        /// <param name="labelColumn">The label column.</param>
         /// <param name="featureColumn">The featureColumn column.</param>
         /// <param name="groupId">The groupId column.</param>
         /// <param name="weights">The optional weights column.</param>
@@ -139,7 +139,7 @@ public static FastTreeRankingTrainer FastTree(this RankingContext.RankingTrainer
         /// Predict a target using generalized additive models trained with the <see cref="BinaryClassificationGamTrainer"/>.
         /// </summary>
         /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
+        /// <param name="labelColumn">The label column.</param>
         /// <param name="featureColumn">The featureColumn column.</param>
         /// <param name="weights">The optional weights column.</param>
         /// <param name="numIterations">The number of iterations to use in learning the features.</param>
@@ -164,7 +164,7 @@ public static BinaryClassificationGamTrainer GeneralizedAdditiveModels(this Bina
         /// Predict a target using generalized additive models trained with the <see cref="RegressionGamTrainer"/>.
         /// </summary>
         /// <param name="ctx">The <see cref="RegressionContext"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
+        /// <param name="labelColumn">The label column.</param>
         /// <param name="featureColumn">The featureColumn column.</param>
         /// <param name="weights">The optional weights column.</param>
         /// <param name="numIterations">The number of iterations to use in learning the features.</param>
@@ -189,7 +189,7 @@ public static RegressionGamTrainer GeneralizedAdditiveModels(this RegressionCont
         /// Predict a target using a decision tree regression model trained with the <see cref="FastTreeTweedieTrainer"/>.
         /// </summary>
         /// <param name="ctx">The <see cref="RegressionContext"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
+        /// <param name="labelColumn">The label column.</param>
         /// <param name="featureColumn">The featureColumn column.</param>
         /// <param name="weights">The optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
@@ -229,7 +229,7 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionContext.Regr
         /// Predict a target using a decision tree regression model trained with the <see cref="FastForestRegression"/>.
         /// </summary>
         /// <param name="ctx">The <see cref="RegressionContext"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
+        /// <param name="labelColumn">The label column.</param>
         /// <param name="featureColumn">The featureColumn column.</param>
         /// <param name="weights">The optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
@@ -269,7 +269,7 @@ public static FastForestRegression FastForest(this RegressionContext.RegressionT
         /// Predict a target using a decision tree regression model trained with the <see cref="FastForestClassification"/>.
         /// </summary>
         /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
+        /// <param name="labelColumn">The label column.</param>
         /// <param name="featureColumn">The featureColumn column.</param>
         /// <param name="weights">The optional weights column.</param>
         /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>

src/Microsoft.ML.HalLearners/HalLearnersCatalog.cs

@@ -4,6 +4,7 @@
 
 using System;
 using Microsoft.ML.Data;
+using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Trainers.HalLearners;
 using Microsoft.ML.Trainers.SymSgd;
 using Microsoft.ML.Transforms.Projections;
@@ -19,36 +20,78 @@ public static class HalLearnersCatalog
         /// Predict a target using a linear regression model trained with the <see cref="OlsLinearRegressionTrainer"/>.
         /// </summary>
         /// <param name="ctx">The <see cref="RegressionContext"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
+        /// <param name="labelColumn">The label column.</param>
         /// <param name="featureColumn">The features column.</param>
         /// <param name="weights">The weights column.</param>
-        /// <param name="advancedSettings">Algorithm advanced settings.</param>
         public static OlsLinearRegressionTrainer OrdinaryLeastSquares(this RegressionContext.RegressionTrainers ctx,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
-            string weights = null,
-            Action<OlsLinearRegressionTrainer.Arguments> advancedSettings = null)
+            string weights = null)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
             var env = CatalogUtils.GetEnvironment(ctx);
-            return new OlsLinearRegressionTrainer(env, labelColumn, featureColumn, weights, advancedSettings);
+            var options = new OlsLinearRegressionTrainer.Options
+            {
+                LabelColumn = labelColumn,
+                FeatureColumn = featureColumn,
+                WeightColumn = weights != null ? Optional<string>.Explicit(weights) : Optional<string>.Implicit(DefaultColumnNames.Weight)
+            };
+
+            return new OlsLinearRegressionTrainer(env, options);
+        }
+
+        /// <summary>
+        /// Predict a target using a linear regression model trained with the <see cref="OlsLinearRegressionTrainer"/>.
+        /// </summary>
+        /// <param name="ctx">The <see cref="RegressionContext"/>.</param>
+        /// <param name="options">Algorithm advanced options. See <see cref="OlsLinearRegressionTrainer.Options"/>.</param>
+        public static OlsLinearRegressionTrainer OrdinaryLeastSquares(
+            this RegressionContext.RegressionTrainers ctx,
+            OlsLinearRegressionTrainer.Options options)
+        {
+            Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+
+            var env = CatalogUtils.GetEnvironment(ctx);
+            return new OlsLinearRegressionTrainer(env, options);
         }
 
         /// <summary>
         ///  Predict a target using a linear binary classification model trained with the <see cref="SymSgdClassificationTrainer"/>.
         /// </summary>
         /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param>
-        /// <param name="labelColumn">The labelColumn column.</param>
+        /// <param name="labelColumn">The label column.</param>
         /// <param name="featureColumn">The features column.</param>
-        /// <param name="advancedSettings">Algorithm advanced settings.</param>
-        public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
+        public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(
+            this BinaryClassificationContext.BinaryClassificationTrainers ctx,
             string labelColumn = DefaultColumnNames.Label,
-            string featureColumn = DefaultColumnNames.Features,
-            Action<SymSgdClassificationTrainer.Arguments> advancedSettings = null)
+            string featureColumn = DefaultColumnNames.Features)
         {
             Contracts.CheckValue(ctx, nameof(ctx));
             var env = CatalogUtils.GetEnvironment(ctx);
-            return new SymSgdClassificationTrainer(env, labelColumn, featureColumn, advancedSettings);
+
+            var options = new SymSgdClassificationTrainer.Options
+            {
+                LabelColumn = labelColumn,
+                FeatureColumn = featureColumn,
+            };
+
+            return new SymSgdClassificationTrainer(env, options);
+        }
+
+        /// <summary>
+        ///  Predict a target using a linear binary classification model trained with the <see cref="SymSgdClassificationTrainer"/>.
+        /// </summary>
+        /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param>
+        /// <param name="options">Algorithm advanced options. See <see cref="SymSgdClassificationTrainer.Options"/>.</param>
+        public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(
+            this BinaryClassificationContext.BinaryClassificationTrainers ctx,
+            SymSgdClassificationTrainer.Options options)
+        {
+            Contracts.CheckValue(ctx, nameof(ctx));
+            Contracts.CheckValue(options, nameof(options));
+            var env = CatalogUtils.GetEnvironment(ctx);
+            return new SymSgdClassificationTrainer(env, options);
         }
 
         /// <summary>
@@ -57,7 +100,8 @@ public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(this
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
         /// <param name="inputColumn">Name of the input column.</param>
-        /// <param name="outputColumn">Name of the column resulting from the transformation of <paramref name="inputColumn"/>. Null means <paramref name="inputColumn"/> is replaced. </param>
+        /// <param name="outputColumn">Name of the column resulting from the transformation of <paramref name="inputColumn"/>.
+        /// Null means <paramref name="inputColumn"/> is replaced. </param>
         /// <param name="kind">Whitening kind (PCA/ZCA).</param>
         /// <param name="eps">Whitening constant, prevents division by zero.</param>
         /// <param name="maxRows">Maximum number of rows used to train the transform.</param>
@@ -69,16 +113,17 @@ public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(this
         /// ]]>
         /// </format>
         /// </example>
-        public static VectorWhiteningEstimator VectorWhiten(this TransformsCatalog.ProjectionTransforms catalog, string inputColumn, string outputColumn = null,
+        public static VectorWhiteningEstimator VectorWhiten(this TransformsCatalog.ProjectionTransforms catalog,
+            string inputColumn, string outputColumn = null,
             WhiteningKind kind = VectorWhiteningTransformer.Defaults.Kind,
             float eps = VectorWhiteningTransformer.Defaults.Eps,
             int maxRows = VectorWhiteningTransformer.Defaults.MaxRows,
             int pcaNum = VectorWhiteningTransformer.Defaults.PcaNum)
                 => new VectorWhiteningEstimator(CatalogUtils.GetEnvironment(catalog), inputColumn, outputColumn, kind, eps, maxRows, pcaNum);
 
         /// <summary>
-        /// Takes columns filled with a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix,
-        /// meaning that they are uncorrelated and each have variance 1.
+        /// Takes columns filled with a vector of random variables with a known covariance matrix into a set of new variables whose
+        /// covariance is the identity matrix, meaning that they are uncorrelated and each have variance 1.
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
         /// <param name="columns">Describes the parameters of the whitening process for each column pair.</param>